# Built for Agents: The Case for Spark Declarative Pipelines

DevFeed: [Built for Agents: The Case for Spark Declarative Pipelines](<https://devfeed.tech/articles/built-for-agents-the-case-for-spark-declarative-pipelines-98506.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/built-agents-case-spark-declarative-pipelines>)

Author: Lisa Cao; Sudhanva Huruli

Published: 2026-10-23T19:00:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Apache Spark](<https://devfeed.tech/topics/spark.md>), [Data pipelines](<https://devfeed.tech/topics/data-pipelines.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Boilerplate](<https://devfeed.tech/topics/boilerplate.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [22](<https://devfeed.tech/tags/22.md>), [3](<https://devfeed.tech/tags/3.md>), [about](<https://devfeed.tech/tags/about.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [data](<https://devfeed.tech/tags/data.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [data-pipelines](<https://devfeed.tech/tags/data-pipelines.md>), [declarative](<https://devfeed.tech/tags/declarative.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [over](<https://devfeed.tech/tags/over.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [production](<https://devfeed.tech/tags/production.md>), [sdp](<https://devfeed.tech/tags/sdp.md>), [share](<https://devfeed.tech/tags/share.md>), [spark](<https://devfeed.tech/tags/spark.md>), [write](<https://devfeed.tech/tags/write.md>)

## AI overview

In a benchmark of 22 data engineering tasks, Spark Declarative Pipelines produced nearly as many successful builds as procedural PySpark (258/264 versus 255/264), while catching structural errors in a dry run before cluster execution. Declarative builds used 17 executor-seconds versus 596 for procedural builds and had a median 64 lines of code versus 131. The approaches had similar silent logic-error rates, so tests and review remain necessary. Declarative builds used about 2.3 times as many LLM tokens--roughly $0.50 versus $0.26 per median build at Opus 4.8 list rates--but the excerpt argues this fixed upfront cost can be offset by avoiding repeated, data-dependent compute waste. Findings are from a specific benchmark, with its setup and results reportedly available in a public repository.

## Source excerpt

AI agents now write a growing share of production data pipelines (over 70% of SDP...